Bibliographic record
Abstract
The purpose of this research is to identify ways in which MacEwan’s CDEL Navigating MyCareer Journey program can better reach students and increase enrollment and completion rates. This paper addresses these goals by looking at ways to improve the programs current marketing strategies and the features that effect its overall appeal. First, we researched fifteen scholarly articles regarding career development and student learning preferences. Next, we conducted three in-depth interviews with MacEwan students; one currently enrolled in the program and two not enrolled. After our analysis, we formulated a questionnaire aimed at solving the programs main areas of concern. A total of 126 respondents from the target group completed the survey. The research indicates that CDEL should focus its marketing efforts on posters, friends, MyMacEwan website, and Blackboard. It also showed that CDEL should focus on creating awareness through friends, parents, online forums, and professors. Our research indicates that networking skills, career/life opportunities, and developing a career mindset are the most important topics to students and that CDEL should focus on those. Our research also identified that making a comprehensive, for-credit course might be the best way to increase their completion rate. We recommend that CDEL develop these key features of the program and modify the course to make it warrant credits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.129 | 0.079 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".